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Record W2996532030 · doi:10.1213/ane.0000000000004597

Pediatric Critical Care—Current Controversies

2019· article· en· W2996532030 on OpenAlexaffabout
Georg M. Schmölzer

Bibliographic record

VenueAnesthesia & Analgesia · 2019
Typearticle
Languageen
FieldMedicine
TopicCongenital Anomalies and Fetal Surgery
Canadian institutionsUniversity of AlbertaRoyal Alexandra Hospital
Fundersnot available
KeywordsMedicineHealth careCritically illIntensive care medicineIntensive careIntensive care unitCritical care nursingVariety (cybernetics)MEDLINENursing

Abstract

fetched live from OpenAlex

There are many controversial topics in the care of critically ill children in pediatric critical care units that will influence the decisions of care made in the daily management of these patients. Health care professionals working in pediatric intensive care are faced with patients with different underlying pathophysiology from adults with a wide range of patient age from 0 to 18 years. This can cause uncertainty among health care professionals concerning the optimal treatment of various clinical scenarios. The authors of Pediatric Critical Care—Current Controversies are recognized and internationally renowned experts in their respective fields who provide an excellent review of problems health care professionals caring for children in the critical care unit experience in their daily practice. The textbook is divided into 8 sections that deal with a variety of diseases and diagnostic dilemmas. The sections are divided according to organ systems, including respiratory and cardiovascular, gastrointestinal, renal, hematological, immunological, endocrine, and neurological issues. Each chapter provides up-to-date diagnostic and treatment approaches for health care professionals working in the pediatric intensive care unit. This is an easy-to-read book about complex pathophysiology and treatment strategies. It is accompanied by illustrations and tables, which simplifies the delivered message. Furthermore, in each chapter, a case report is discussed and is supported by current evidence from the most important and most recent publications, along with expert opinion from the wealth of experience of the authors. At the end of each chapter, there are take-home messages. This approach provides the reader with a more in-depth understanding on how to treat these specific patients. It should also be noted that the important topics of nutritional support, fluid overload, and sedation are found prominently in the book, emphasizing the need for consistent management and implementation of institutional protocols to improve outcome in these critically ill pediatric patients. The diagnosis of brain death for the purpose of organ donation is an ethically difficult topic that provides the basis for an in-depth discussion using several patient scenarios. One weakness appears to be the lack of inclusion of surgical issues in neonatal patients (eg, necrotizing enterocolitis and gastroschisis). Indeed, many pediatric intensive care units also care for neonatal patients in general and surgical patients more specifically. Care of such patients includes optimal surgical management along with optimal ventilation strategies (eg, targeted tidal volume) and feeding management. Pediatric Critical Care—Current Controversies provides an excellent and detailed overview and is a valuable educational and practical resource for residents, critical care fellows, critical care attending physicians, and anyone else who is involved in the treatment of critically ill pediatric patients in the pediatric intensive care unit. Georg M. Schmölzer, MD, PhDCentre for the Studies of Asphyxia and ResuscitationNeonatal Research UnitRoyal Alexandra HospitalEdmonton, Alberta, CanadaDepartment of PediatricsUniversity of AlbertaEdmonton, Alberta, Canada[email protected]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.007
Scholarly communication0.0050.009
Open science0.0030.002
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0100.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.261
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2019
Admission routes2
Has abstractyes

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